Global motion adaptive system with motion values correction with respect to luminance level

ABSTRACT

Global-adaptive deinterlacing systems and methods for reducing scintillation and feathering artifacts. Motion adaptive deinterlacing (MADI) local motion quantization thresholds are adaptively adjusted according to the amount of global motion present in the video sequence, thereby minimizing scintillation and feathering artifacts when deinterlacing the fields. A set of global motion scenarios are defined for the purpose of classifying fields, and a number of global motion indicators are used to detect on a field-by-field basis different global motion scenarios.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a Continuation in Part of U.S. patentapplication Ser. No. 11/143,510 filed on Jun. 1, 2005 now U.S. Pat. No.7,471,336, entitled that takes priority under 35 U.S.C. 119(e) to U.S.Provisional Patent Application No. 60/654,263 filed on Feb. 18, 2005entitled “GLOBAL MOTION ADAPTIVE SYSTEM WITH MOTION VALUES CORRECTIONWITH RESPECT TO LUMINANCE LEVEL” that is incorporated by reference inits entirety.

FIELD OF INVENTION

The invention pertains in general to motion adaptive deinterlacing, andin particular to systems and methods for adaptively adjusting localmotion thresholds according to global motion indicators in order toreduce scintillation and feathering artifacts.

BACKGROUND

Interlaced video signals comprise two video fields, one for the oddlines and one for the even lines of an image. This is due to the imagecapture process, wherein the camera outputs the odd lines at one instantin time and the even lines slightly later. This creates a temporal shiftbetween the odd and even lines of the image, which needs to be addressedin frame based processing systems. A deinterlacing process generallyattempts to overcome this problem by assembling a clean frame from thetwo fields.

Since the temporal shift between the two fields introduce feathering andscintillation artifacts, motion adaptive deinterlacing (MADI) techniqueshave been proposed in order to reduce such artifacts. Some MADItechniques use local motion threshold values that can be adjustedmanually in order to improve the performance of the de-interlacer on aspecific problematic video sequence, albeit possibly at the cost ofsacrificing the performance (and re-introducing de-interlacingartifacts) in other video sequences.

Therefore, instead of manually adjusting the MADI thresholds in order to“Pass” a specific video sequence, it is desirable to develop a newadaptive system that adjusts the local MADI thresholds automatically andadaptively.

SUMMARY OF THE INVENTION

Disclosed are global-adaptive deinterlacing systems and methods forreducing scintillation and feathering artifacts. MADI local motionquantization thresholds are adaptively adjusted according to the amountof global motion present in the video sequence, thereby minimizingscintillation artifacts (generally present in low motion images) andfeathering artifacts (generally present in high motion images) whendeinterlacing the fields. A set of global motion “scenarios” are definedfor the purpose of classifying fields, and a number of global motionindicators are used to detect on a field-by-field basis different globalmotion scenarios. The global motion indicators are corrected to reduceLuma dependencies, thereby improving reliability and robustness.Depending on the global motion scenario of a field, the local motionthresholds are adaptively adjusted. The adaptive adjustment ofquantization thresholds are optionally also applied to temporal noisereduction and cross-color suppression sub-systems.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIGS. 1 a,b show an example image from a sample video sequence (movingpendulum) and its associated global motion R and S signals.

FIG. 2 a is a 3-D graph showing an R global motion signal as a functionof Luma level obtained from a sample video sequence.

FIG. 2 b is a 2-D graph showing a normalized R global motion signal as afunction of Luma level.

FIG. 3 a is a 3-D graph showing an S global motion signal as a functionof Luma level obtained from a sample video sequence.

FIG. 3 b is a 2-D graph showing a normalized S global motion signal as afunction of Luma level.

FIG. 4 a shows a moving pendulum with the Luma level slightly decreasedcausing feathering artifacts to start to appear.

FIG. 4 b shows the same moving pendulum with Luma level set to 100%. Inthis case the system is tuned and the image is displayed withoutfeathering artifacts.

FIG. 4 c shows the same moving pendulum with Luma level decreased to thepoint in which the moving pendulum shows a high degree of featheringartifacts.

FIGS. 5 a,b illustrate examples of R and S global motion signalscorrection functions.

FIG. 6 a is a graph showing an R global motion signal (not corrected) asa function Luma level and motion speed, generated from a sample videosequence. The S signal behaves similarly.

FIG. 6 b is a graph showing a corrected R_(corr) global motion signal,compensated for the observed dependence on the Luma value. The S_(corr)is corrected similarly.

FIG. 7 is a graph of the S_(corr) global motion signal for a samplevideo sequence together with the three regions indicating three globalmotion scenarios. These same three regions are defined for the R_(corr)signal as well. The regions are determined by defining the global motionthresholds Tlow and Thigh.

FIG. 8 is a table summarizing various considered global motion scenariosand their combinations.

FIG. 9 shows two graphs of the S_(corr) and dS_(corr) signals andindicates a count of the number of zero crossings of dS_(corr). ThedS_(corr) difference signal is used to determine the presence ofvertical motion pattern in the image.

FIG. 10 is a flow chart (high-level) illustrating a method for globaladaptive deinterlacing.

FIG. 11 a above shows the tap structure when the current input field isODD parity. Taps from three successive input video fields areconsidered.

FIG. 11 b shows the equivalent tap structure when the current field isEVEN parity. Each tap is itself horizontally filtered over severalpixels in a line of the field.

FIG. 12 shows a flow diagram illustrating a method for global adaptivedeinterlacing, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to a particular embodiment of theinvention, examples of which are illustrated in the accompanyingdrawings. While the invention will be described in conjunction with theparticular embodiments, it will be understood that it is not intended tolimit the invention to the described embodiments. To the contrary, it isintended to cover alternatives, modifications, and equivalents as may beincluded within the spirit and scope of the invention as defined by theappended claims.

Motion Adaptive Deinterlacing and Local Motion Quantization Thresholds

An interlaced video signal comprises odd fields (lines 1, 3, 5, . . . )and even fields (lines 2, 4, 6, . . . ). A deinterlacing videoprocessing system produces a progressive video output signal from aninterlaced video input signal for viewing on a progressive displaydevice. Instead of performing simple fields merging (i.e. static mesh)to deinterlace the input video, a combination of vertical interpolationand temporal interpolation is performed in order to obtain a highquality deinterlaced image frame which has a minimum of flickeringartifacts in the static areas and a minimum of feathering artifacts inthe moving areas. In order to better achieve this goal, local motion(such as on a pixel-by-pixel basis) in the input sequence can beestimated in order to separately process the static and the movingportions of the image depending upon the presence and level of localmotion in the vicinity of each pixel. This is referred to as motionadaptive deinterlacing (MADI), and two main tasks of such a MADI systemare:

1. Motion detection, comprising detecting the level of local motion foreach pixel and/or its neighborhood; and

2. Deinterlacing, thereby producing a progressive frame.

In order to detect the presence of local motion in the input videosequence, two fields of the same polarity (i.e. even and even, or oddand odd) are used by the MADI system in order to compute the valuedifferences (i.e. temporal variation) between each two pixels having thesame coordinates in the two fields. In addition, differences betweenpixel values of two adjacent fields in the vertical direction may becomputed in order to recognize the presence of vertical motion. The MADIsystem then estimates local motion values for each pixel based on theobtained temporal variation, vertical variation and optionally globalnoise present in the video signal. Once estimated, the local motionvalues are quantized into a number of levels as indicated by a set ofMADI local motion quantization thresholds, which define a set of localmotion ranges. The pixels are then deinterlaced by a set of availablemethods according to the quantized local motion values.

Global-Adaptive Deinterlacing System

While a MADI system as described above does reduce deinterlacingartifacts, scintillation and feathering artifacts are still present inthe final deinterlaced sequence. Disclosed herein are global-adaptivedeinterlacing systems and methods for reduce scintillation andfeathering artifacts by adaptively adjusting the MADI local motionquantization thresholds according to the amount of global motion presentin the video sequence. A set of global motion “scenarios” are definedfor the purpose of classifying sequences, and a number of global motionindicators are used to detect on a field-by-field basis different globalmotion scenarios. Depending on the global motion scenario of a field,the local motion thresholds are dynamically adjusted, thereby minimizingscintillation artifacts (generally present in low motion images) andfeathering artifacts (generally present in high motion images) whendeinterlacing the field.

Global Motion Indicators: R and S Signals

Two signals, hereinafter referred to as R and S signals, are used asinitial indicators of the amount of global motion present in the inputimage. These signals may be generated by a Film Mode detection block ofthe de-interlacer system, or they may be separately computed. The R andS signals are defined as functions of the incoming fields as follows:

c = curr_field Luma values of the current field in the interlaced videosignal (can be even or odd) p = prev_field Luma values of the previousfield (in time) in the interlaced video signal (when c is even p is odd,and vice versa). p⁻¹ = field previous to p Luma values of the fieldprevious to p (in time). R = c − p⁻¹

R = sum(c − p⁻¹). Pixel-to-pixel subtraction and conversion of result toa scalar R = sum(c − p⁻¹). S = c − p

S = sum(c − p). Pixel-to-pixel subtraction and conversion of result to ascalar S = sum(c − p).

Note that when the interlaced video signal comprises little globalmotion, c and p are close in value and their difference is small.Furthermore, when an object moves from an odd field to an even field (orvice versa), the spatial shift causes a corresponding increase in theresult of the subtractions. An example image from a sample videosequence is shown in FIG. 1 a, and the R and S signals for the sequenceare shown in FIG. 1 b.

Correcting the R and S Signals

Since the R and S global motion indicators are obtained by performingLuma subtractions between different video fields, the signals aredependent on the Luma levels. For example, if a specific video sequencecomprising objects moving at a fixed speed is played with two differentLuma levels (i.e. two different brightness levels), the two obtainedsets of R and S values corresponding to each Luma level will bedifferent.

FIGS. 2 a,b and 3 a,b are 3-D and 2-D graphs showing actual samples of Rand S values and illustrating how these global motion values varyexponentially with respect to the maximum Luma value contained in theimage. FIG. 2 a plots the R signal as a function of Luma level and asample video sequence (“R sample”), and FIG. 2 b shows the R signal as afunction of Luma level. FIG. 3 a plots the S signal as a function ofLuma level and a sample video sequence (“S sample”), and FIG. 3 b showsthe S signal as a function of Luma level. The graphs were generated withthe same sample video sequence.

When the maximum Luma levels in the incoming video fields vary, themagnitude of the R and S signals are altered (scaled) by a factorƒ(Y_(max)). Therefore, from this moment we will refer to the R and Ssignals as the uncorrected S_(uncorr) and R_(uncorr) signals.S _(uncorr) =S·ƒ(Y _(max))R _(uncorr) =R·ƒ(Y _(max))

where Y_(max) represents the maximum Luma value in the video field. Thefunction ƒ is an exponential function as the ones shown in FIGS. 2 b and3 b.

The Luma dependency problem affects the performance of the deinterlaceras illustrated by the examples of FIGS. 4 a,b,c all having the samefixed local motion threshold quantizer settings. The local motionthresholds were adjusted in such a way that the moving object startsshowing feathering artifacts when the Luma level is set to the defaultvalue of 100%. FIG. 4 a shows a moving pendulum with the Luma level setto the default 100%. In this case the moving pendulum starts to showfeathering. FIG. 4 b shows a the same moving pendulum moving at the samespeed but with the Luma set to 120%. In this case the featheringartifacts do not appear because the MADI system produced avertically-filtered de-interlaced image as a result of the higher localmotion values being above the fixed local thresholds.

In contrast, FIG. 4 c shows the same moving pendulum moving at the samespeed but with the Luma set to 80%. In this case the featheringartifacts clearly appear on the image because the MADI system produced afiled-paired (static-mesh) de-interlaced image as a result of the lowerlocal motion values being below the fixed local thresholds.

In order to remove or at least reduce the dependency on the Luma level,an inverse function

${g\left( Y_{\max} \right)} = \frac{1}{f\left( Y_{\max} \right)}$is defined for multiplication by the R_(uncorr) and S_(uncorr) values asfollows:

$S_{corr} = {\frac{1}{g\left( Y_{\max} \right)} \cdot S_{uncorr}}$$R_{corr} = {\frac{1}{g\left( Y_{\max} \right)} \cdot R_{uncorr}}$

Where the S_(corr) and R_(corr) corrected values represent acceptableapproximations of the desired R and S indicators.

By way of example, the inverse function g(Y_(max)) may be defined as a256-entry (for an 8-bit system) 8.8 Fixed-Point format look-up table(for example implemented in firmware or hardware). FIGS. 5 a,b showexamples of correction functions.

In these correction functions LUT, an entry is selected according to themaximum Luma level (index) detected in the field. Then the entry ismultiplied by the S_(uncorr) and R_(uncorr) values. In the showncorrection functions the entries corresponding to Luma values lower than128 (50%) are clipped in order to avoid over-correction caused by adivision by zero. For Luma values equal or higher than 235 (ITU???-601standard) the correction value is clipped to 1.

In order to correct the pairs of S_(uncorr) and R_(uncorr) values for agiven field, the maximum Luma level of the field is determined. Themaximum Luma value can be directly computed, or may simply be availablefrom the deinterlacing system. For example, a MADI chip may have aregister making available the maximum Luma value detected in the currentfield. This value is then used as an index to a correction look-up tablein order to properly select a correction factor which will be multipliedwith the S_(uncorr) and R_(uncorr) values to obtain the S_(corr) andR_(corr) values that will be used as the corrected global motionindicators.

FIGS. 6 a,b are 3-D graphs showing an example R signal before and aftercorrection. FIG. 6 a is a graph illustrating an uncorrected R signal asa function Luma value and motion speed, generated from a video sequence.Note that reducing the Luma value in turn reduces the R_(uncorr) signal,while as a motion indicator it is desirable to have the R_(uncorr)signal be independent of the Luma value. Accordingly, as shown in FIG. 6b, the signal is corrected (for the Luma level range of about 50-100%),compensating for the observed dependence on the Luma value.

This avoids the reduction of the global motion magnitude values andhence generates a reliable (nearly-constant) pair of S_(corr) andR_(corr) values (for the range of Luma levels 50-100%). Therebyimproving de-interlacer robustness by retaining the high performance ofthe deinterlacer in dark scenes.

Motion Scenarios

A set of motion scenarios is defined in order to classify fieldsdepending on the level of global motion and presence of vertical motionpattern. The following scenarios are used:

Low motion scenario: Indicating still images or very low global motionimages.

Medium motion scenario: Indicating medium global motion images.

High motion scenario: Indicating high global motion images.

Vertical motion scenario: Indicating images with vertical global motionpattern.

Given an image in a sequence, a scenario is determined based on theR_(corr) and S_(corr) signals. The scenario indicates appropriateadjustments to the local motion thresholds according to the amount ofglobal motion and/or the presence of vertical motion in the image. Byway of example, the following definitions have been found to work well:

S_(corr) and R_(corr) values below the threshold value of Tlow=3 (higher16 bits of a 32-bit word) indicating a low global motion scenario,S_(corr) and R_(corr) values between the Tlow=3 and Thigh=4800indicating a medium global motion scenario, and S_(corr) and R_(corr)values above Thigh=4800 indicating a high global motion scenario. FIG. 7shows a graph of the S_(corr) signal for a sample video sequencetogether with three arbitrary regions indicating the global motionscenarios. The table of FIG. 8 summarizes various considered globalmotion scenarios and their combinations. Currently, only the non-shadedrows are being considered.

Adjusting Quantizer Threshold Values of a Motion Adaptive De-Interlacer

Generally, the local motion value of a specific pixel in a given fieldare determined by computing a function of pixel Luma and/or Chroma,

involving the specific pixel (and its neighbors) contained in thecurrent, previous and previous⁻¹ fields.

Because of cost and computational overhead reasons, the obtained localmotion value for the analyzed pixel is quantized hence reducing itsresolution.

If the thresholds in the quantizer are adjustable, then the distributionof local motion codes can be adjusted accordingly.

Therefore, adjusting the quantizer threshold values essentiallyredistributes the sensitivity of the quantizer. If the quantizerthresholds are set to a relatively low value, then the Motion AdaptiveDe-interlacer will treat most of the pixels as if they had high localmotion. Consequently, slow moving objects will exhibit scintillationartifacts.On the other hand, if the quantizer thresholds are set to a relativelyhigh value, then the Motion Adaptive De-interlacer will treat most ofthe pixels as if they had low local motion. Consequently, fast movingobjects will exhibit feathering artifacts.

As described above, the present invention adjusts the local motionquantization thresholds according to the amount of global motion. As anexample, the local motion values may be originally represented by 8-bitnumbers and subsequently quantized by a 2-bit quantizer into four levelsindicated by a set of three local motion quantization thresholdsMADI_QUANT_THRESH₀, MADI_QUANT_THRESH₁ and MADI_QUANT_THRESH₂, asfollows:

Local motion level 0: local motion<MADI_QUANT_THRESH₀

Local motion level 1: MADI_QUANT_THRESH₀≦local motion<MADI_QUANT_THRESH₁

Local motion level 2: MADI_QUANT_THRESH₁≦local motion<MADI_QUANT_THRESH₂

Local motion level 2: MADI_QUANT_THRESH₂≦local motion

Thus, in this example, a quantized local motion value obtained for apixel indicates which one of four available deinterlacing methods willbe used to deinterlace the pixel.

Feathering artifacts occur when the pixels and lines of a moving objectare wrongly deinterlaced by the use of the “fields pairing” technique.The effect is a misalignment of the moving object pixels visible ashorizontal lines. This problem can be improved by lowering the values ofthe local motion thresholds. Scintillation artifacts occur when thepixels and lines of a static object are wrongly deinterlaced by the useof the “spatial processing” (vertical filtering) technique. The effectis a flickering artifact on the static object pixels. This problem canbe improved by raising the values of the local motion thresholds.

The thresholds start out with a set of “default” values. By way ofexample, default values of approximately MADI_QUANT_THRESH₀=6,MADI_QUANT_THRESH₁=8 and MADI_QUANT_THRESH₂=15 have been found to workwell. In order to deinterlace an incoming field, first a global motionscenario is identified for the field as described above. If the fieldexhibits a medium motion scenario, the default thresholds remain inplace (or are reverted to) and used by the quantizer to determine localmotion values for the pixels in the field and choose a deinterlacingmethod accordingly. However, if the field comprises a low motionscenario, the probability of scintillation artifacts to occur getsincreased. In order to prevent the presence of these artifacts, thelocal motion regions in the quantizer are re-distributed accordingly byraising the local motion thresholds.

By way of example, an adjustment of approximately MADI_QUANT_THRESH₀=13,MADI_QUANT_THRESH₁=14 and MADI_QUANT_THRESH₂=15 has been found to workwell.

On the other hand, if the field comprises a high motion scenario, thethresholds are lowered accordingly. By way of example, an adjustment ofapproximately MADI_QUANT_THRESH₀=4, MADI_QUANT_THRESH₁=5 andMADI_QUANT_THRESH₂=15 has been found to work well. As a result, presenceof global motion appropriately affects the local motion regions used bythe deinterlacer in order to reduce artifacts such as scintillation andfeathering.Vertical Motion Pattern Detection

Vertical motion may cause artifacts during a medium motion scenario.Vertical global motion is detected in a number of ways. One approachmakes use of the S_(corr) signal, as shown in the graph of FIG. 9. Abuffer is defined for computing the ongoing incremental differencedS_(corr) between the current value and the previous values of S_(corr),namely: dS_(corr)=(current S_(corr))−(previous S_(corr)). The incomingsamples of dS_(corr) are analyzed on the fly by counting the number ofzero crossings, as shown in FIG. 9. If the zero crossings count for aspecific period exceeds a specified threshold (e.g. 10), this is anindication that a periodical pattern was detected (which is oftenexhibited by vertical motion). Following such a detection, the magnitudeof the S_(corr) analyzed in order to detect specific scenarios (e.g.vertical pattern and medium global motion), and therefore performadjustments in the local motion quantizer that produce improvements inthe displayed image.

Another approach to vertical motion detection utilizes a vertical motiondetector element that detects vertical global motion by way of a fourstage process. First, vertical motion of individual pixels is detected.These motion values are used to determine the vertical motion of tileswhich divide the input field. Last, the motion values of the tiles in afield are used to determine whether that entire field is judged to be invertical motion. FIG. 11 a above shows the tap structure when thecurrent input field is ODD parity. Taps from three successive inputvideo fields are considered. Taps A and C are from the current inputfield, B, D, and E are from the previous field of opposite parity to thecurrent field, and C′ is from the previous field of the same parity asthe current field. FIG. 11 b shows the equivalent tap structure when thecurrent field is EVEN parity. Each tap is itself horizontally filteredover several pixels in a line of the field.

The pixel vertical motion is calculated by comparing the top- andbottom-most pixel in the current field with the pixels in the previousfield of opposite parity. For example, with an ODD current field, if tapC show high correlation to either tap E and/or tap B, the pixel is saidto have downward motion. To prevent motion aliasing, detection ofdownward motion is predicated on the pixel in the corresponding locationin the previous field of the same parity being uncorrelated with the tapin the current field (for example, C and C′ must be uncorrelated).Correlation can be measured with either a simple absolute difference orvia more elaborate correlation functions. Pixels in vertical motion thencause a tile counter to be either incremented or decremented based ontheir direction of motion.

The taps in the current field are divided spatially into rectangulartiles. With each tile is associated a counter, which increments ofdecrements according to the pixel motion calculated above. The tilecounter provides a measure of the overall motion of pixels within thetiles: if the tile counter is positive, the pixels are, on average,moving upwards, and if negative, downwards. The counter value iscompared with a threshold to provide noise immunity; if the threshold isexceeded, the tile is judged to be in vertical motion. A field verticalmotion counter is incremented or decremented based on the direction ofthe tile motion.

Next, the field motion counter is compared with another threshold todetermine if the entire field has vertical motion. This threshold isadaptive based on attributes of the input field, such as parity. Thiscreates a two bit output signal which indicates if the field is invertical motion, and if so, the direction of motion, up or down.

Finally, a history of the vertical motion is kept over several fields.If this vertical motion is consistent and in the same direction overseveral fields, a high degree of confidence that vertical motion is realexists. In this case a final one-bit output signal indicates that theinput sequence contains vertical motion. This output signal useshysteresis to prevent noise from causing quick successive changes in thedetected vertical motion.

FIG. 12 shows a flow diagram illustrating a method for global adaptivedeinterlacing, in accordance with an embodiment of the presentinvention. At step 1202, if the input port or video is changed, fetchthe standard (default) MADI location motion threshold values at step1203, load them into the MADI local motion quantizer at step 1204, andsetup the contrast tool parameters at step 1205, and proceed to step1206. Otherwise, proceed directly to step 1206. At step 1206, ifGL_MADI_EN=0 (i.e. if the global-adaptive system is not enabled) then donot apply the adaptive algorithm. Else (i.e. if the global-adaptivesystem is enabled), get the contrast distribution and determinecorrection factor at step 1207. At step 1208, read the R and S signalsand correct the R and S signals at step 1209, determine the globalmotion scenario at step 1210 and determine presence of vertical motionat step 1211. At step 1212, adjust the MADI local motion thresholdsaccording to the global motion scenario and presence of vertical motion,and complete the process by proceeding to step 1213.

Temporal Noise Reduction

Optionally, the deinterlacer may comprise a temporal noise reduction(TNR) component, having a separate local motion values quantizer forincreased flexibility. In such an embodiment, an initial noisemeasurement may be obtained from a sub-system of the deinterlacer,wherein such a sub-system may comprise a noise meter and optionally somedigital filtering to produce a reliable noise measurement. This noisemeasurement may then be combined with the global motion indicatorsS_(corr) and R_(corr) to automatically adjust both the amount of noisereduction and the local motion thresholds according to the measurednoise and global motion. The objective of using the global motion valuesis to avoid “ghosting” artifacts (blurring) on moving objects that occurwhen the amount of noise reduction is relatively high in the high-motionobjects. The objective of using the noise measurements is to adjust theamount of noise reduction according to the noise present in the image.

Cross Color Suppression

Optionally, the deinterlacer may comprise a cross color suppression(CCS) component, having a separate local motion values quantizer forincreased flexibility. In such an embodiment, the quantizer thresholdscan be adjusted based on the global motion indicators S_(corr) andR_(corr) in order to reduce blurring of colors and ghosting artifacts invideo sequences involving motion.

FIG. 10 shows a flow diagram illustrating a method for global adaptivedeinterlacing, in accordance with an embodiment of the presentinvention. At step 202, if the input port of video is changed, fetch thestandard (default) MADI location motion threshold values at step 203,load them into the MADI local motion quantizer at step 204, and setupthe MinMax tool parameters (this tool provides the maximum Luma value inthe current field) at step 205, and proceed to step 206. Otherwise,proceed directly to step 206. At step 206, if GL_MADI_EN=0 (i.e. if theglobal-adaptive system is not enabled, go to step 217 (i.e. do not applythe adaptive algorithm). Else (i.e. if the global-adaptive system isenabled), get the maximum Luma value for the current field at step 207.At step 208, if the maximum Luma value is less than 50% (i.e. the globalmotion correction can not be applied), load the standard MADI localmotion thresholds into the local motion quantizer at step 209, andproceed to step 217, which completes the process. Otherwise (i.e. theglobal motion correction can be applied), read the R and S signals atstep 211, correct the R and S signals at step 212, determine the globalmotion scenario at step 213 and determine presence of vertical motion atstep 214. At step 215, adjust the MADI local motion thresholds accordingto the global motion scenario and presence of vertical motion, andcomplete the process by proceeding to step 217.

Foregoing described embodiments of the invention are provided asillustrations and descriptions. They are not intended to limit theinvention to precise form described. Other variations and embodimentsare possible in light of above teachings, such as implementing thedescribed embodiments in hardware or software.

1. A global adaptive deinterlacing method for reducing feathering andscintillation artifacts, comprising: determining a contrastdistribution; determining a correction factor; reading an R signal andan S signal, each an initial indicator of global motion in an inputimage; determining a global motion regions based upon the R and Ssignals; and detecting vertical motion of pixels, thereby derivingvertical motion values; determining vertical motion of tiles using saidvertical motion values and a tile counter; and determining whether anentire field is in vertical motion using the vertical motion of tilesand a field motion counter, wherein the determining an amount of globalmotion comprises generating and reading one or more provided globalmotion indicator signals.
 2. The method of claim 1, wherein thedetermining an amount of global motion comprises computing Lumadifferences between a current field and a previous field to obtain oneor more global motion indicator signals.
 3. The method of claim 2,wherein the determining an amount of global motion further comprisesobtaining the maximum Luma value for the current field which is used tofetch a correction factor that is multiplied by the uncorrected globalmotion values in order to perform correction of those global motionvalues.
 4. The method of claim 3, wherein the determining an amount ofglobal motion further comprises using the maximum Luma value to correctthe global motion indicator signals and thereby reduce Luma dependency.5. The method of claim 1, further comprising determining the presence ofvertical motion pattern in the video sequence.
 6. The method of claim 1,further comprising adjusting one or more temporal noise reductionquantizer thresholds according to the amount of global motion.
 7. Themethod of claim 1, further comprising adjusting one or more cross colorsuppression local motion quantizer thresholds according to the amount ofglobal motion.
 8. A global adaptive deinterlacing system for reducingfeathering and scintillation artifacts, comprising: a controller for:determining an amount of global motion present in a video sequence;adjusting one or more local motion quantizer thresholds according to theamount of global motion, wherein the determining an amount of globalmotion comprises reading one or more provided global motion indicatorsignals; detecting vertical motion of pixels, thereby deriving verticalmotion values; determining vertical motion of tiles using said verticalmotion values and a tile counter; and determining whether an entirefield is in vertical motion using the vertical motion of tiles and afield motion counter.
 9. The system of claim 8, wherein the determiningan amount of global motion comprises computing Luma differences betweena current field and a previous field to obtain one or more global motionindicator signals.
 10. The system of claim 9, wherein the determining anamount of global motion further comprises obtaining the maximum Lumavalue for the current field.
 11. The system of claim 10, wherein thedetermining an amount of global motion further comprises using themaximum Luma value to correct the global motion indicator signals andthereby reduce Luma dependency.
 12. The system of claim 8, thecontroller further for: determining the presence of vertical motionpattern in the video sequence.
 13. The system of claim 8, the controllerfurther for: adjusting one or more temporal noise reduction quantizerthresholds according to the amount of global motion.
 14. The system ofclaim 8, the controller further for: adjusting one or more cross colorsuppression local motion quantizer thresholds according to the amount ofglobal motion.